Making group inferences using sparse representation of resting-state functional mRI data with application to sleep deprivation

4 years ago

Making group inferences using sparse representation of resting-state functional mRI data with application to sleep deprivation

Jian Qin, Liu Yang, Lubin Wang, Peng Zhang, Yu Lei, Huaze Xu, Dewen Hu, Zheng Yang, Zongtan Zhou, Ling-Li Zeng, Hui Shen

Past studies on drawing group inferences for functional magnetic resonance imaging (fMRI) data usually assume that a brain region is involved in only one functional brain network. However, recent evidence has demonstrated that some brain regions might simultaneously participate in multiple functional networks. Here, we presented a novel approach for making group inferences using sparse representation of resting-state fMRI data and its application to the identification of changes in functional networks in the brains of 37 healthy young adult participants after 36 h of sleep deprivation (SD) in contrast to the rested wakefulness (RW) stage. Our analysis based on group-level sparse representation revealed that multiple functional networks involved in memory, emotion, attention, and vigilance processing were impaired by SD. Of particular interest, the thalamus was observed to contribute to multiple functional networks in which differentiated response patterns were exhibited. These results not only further elucidate the impact of SD on brain function but also demonstrate the ability of the proposed approach to provide new insights into the functional organization of the resting-state brain by permitting spatial overlap between networks and facilitating the description of the varied relationships of the overlapping regions with other regions of the brain in the context of different functional systems. Hum Brain Mapp 38:4671–4689, 2017. © 2017 Wiley Periodicals, Inc.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/hbm.23693